Step-3.7-Flash: The New Open-Weight King of Multimodal Agentic AI

• ClawEval-1.1: 67.1 (#1)
• SimpleVQA Search: 79.2 (#1)
• V* Python: 95.3
• SWE-PRO: 56.3 (#2)
• τ²-bench: 98%+ (Reliable Tool Use)

Native Multimodality & Web Search

Unlike models that use separate vision encoders bolted onto a text backbone, Step-3.7-Flash is natively multimodal. This means it doesn't just 'see' an image; it understands the semantic relationship between UI elements, charts, complex documents, and code. This makes it uniquely capable of tasks like 'Look at this dashboard screenshot and write a Python script to replicate this chart.'

The model also features an advanced web and visual search capability. It can browse multiple sources and perform deep follow-up queries to verify information. This makes it a formidable tool for RAG (Retrieval-Augmented Generation) workflows where accuracy and source verification are paramount.

• Native UI and Document Understanding
• Advanced Chart and Image Interpretation
• Deep Web + Visual Search with multi-source follow-ups
• Seamless transition from visual input to code output

API Pricing: Unbeatable Efficiency

StepFun has optimized the pricing model to be incredibly competitive for high-volume developers. By utilizing a cache-hit mechanism, developers can drastically reduce costs for repetitive prompts or long-context RAG applications. The combination of high throughput and low cost makes it one of the most economically viable models for scaling agentic services.

The pricing structure is transparent and designed to reward efficient prompt engineering through significant discounts on cache hits.

• Highly optimized for high-concurrency workloads
• Significant cost savings via Cache Hit pricing
• Ideal for long-context RAG and agentic loops

Use Cases: From Coding to Autonomous Agents

The versatility of Step-3.7-Flash allows it to serve multiple high-impact domains. In software engineering, its high V* Python and SWE-PRO scores make it a premier choice for automated code review, debugging, and boilerplate generation. In the realm of business intelligence, its ability to parse charts and documents allows for automated reporting and data extraction.

For AI engineers building autonomous agents, the 98%+ reliability on τ²-bench is the headline feature. You can deploy this model into complex loops where it must interact with external APIs, navigate web interfaces, and make decisions based on visual feedback without constant human intervention.

• Automated Software Engineering & Debugging
• Visual Data Extraction & Business Intelligence
• Autonomous Web-Navigating Agents
• Advanced Multimodal RAG Systems

Getting Started

Ready to integrate Step-3.7-Flash into your stack? Because the weights are released under the Apache 2.0 license, you can host the model locally using frameworks like GGUF or NVIDIA NIM for maximum privacy and control. For those preferring a managed service, the model is available via API through providers like OpenRouter.

For local deployment, check Hugging Face for BF16, FP8, and NVFP4 versions, as well as GGUF quantizations to fit various hardware constraints.

• Local Deployment: Available on Hugging Face (BF16, FP8, GGUF)
• Managed API: Available via OpenRouter
• License: Apache 2.0 (Open Weights)

API Pricing — Input: $0.20 / 1M tokens / Output: $1.15 / 1M tokens / Context: 256K

Sources